Neuronovo/neuronovo-9B-v0.1
Neuronovo/neuronovo-9B-v0.1 is a 7 billion parameter large language model, fine-tuned from teknium/OpenHermes-2.5-Mistral-7B. It leverages LoRA and DPO training on the Intel/orca_dpo_pairs dataset, focusing on efficient adaptation for dialogue systems. This model is optimized for advanced language generation tasks, particularly in interactive dialogue contexts, with a 4096 token context length.
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Overview
Neuronovo/neuronovo-9B-v0.1 is a 7 billion parameter large language model, fine-tuned from the teknium/OpenHermes-2.5-Mistral-7B base model. It is specifically adapted for dialogue systems, utilizing the Intel/orca_dpo_pairs dataset for training. The model employs advanced fine-tuning techniques like LoRA (Low-Rank Adaptation) and a custom DPO (Data Parallel Optimization) Trainer to achieve efficient and effective adaptation.
Key Capabilities
- Dialogue Generation: Specialized training on a dialogue-focused dataset makes it proficient in generating conversational text.
- Efficient Fine-tuning: Uses LoRA with specific parameters (r=16, lora_alpha=16) to adapt the model efficiently while preserving pre-trained weights.
- Advanced Training: Incorporates a DPO Trainer, cosine learning rate scheduler, paged AdamW optimizer, and 4-bit precision for optimized training of large models.
- Causal Language Modeling: Configured for text generation and continuation, supporting a maximum prompt length of 1024 tokens and generation length of 1536 tokens.
Good For
- Interactive Dialogue Systems: Its fine-tuning on dialogue pairs makes it suitable for chatbots and conversational AI applications.
- Efficient Adaptation: Developers looking to adapt a powerful base model to specific tasks with computational efficiency.
- Text Generation: General text generation tasks where extended context and output lengths are beneficial.